funasr v2 setup

游雁

2023-11-21 c644ac8f58895b9e29e9cfca79465fd2c0efaa5a

 funasr/train/trainer.py

@@ -26,7 +26,6 @@
import torch
import torch.nn
import torch.optim
from typeguard import check_argument_types

from funasr.iterators.abs_iter_factory import AbsIterFactory
from funasr.main_funcs.average_nbest_models import average_nbest_models
@@ -39,11 +38,12 @@
from funasr.torch_utils.device_funcs import to_device
from funasr.torch_utils.recursive_op import recursive_average
from funasr.torch_utils.set_all_random_seed import set_all_random_seed
from funasr.train.abs_espnet_model import AbsESPnetModel
from funasr.models.base_model import FunASRModel
from funasr.train.distributed_utils import DistributedOption
from funasr.train.reporter import Reporter
from funasr.train.reporter import SubReporter
from funasr.utils.build_dataclass import build_dataclass
from funasr.utils.kwargs2args import kwargs2args

if torch.distributed.is_available():
    from torch.distributed import ReduceOp
@@ -95,6 +95,7 @@
    use_pai: bool
    oss_bucket: Union[oss2.Bucket, None]
    batch_interval: int
    bias_grad_times: float

class Trainer:
    """Trainer having a optimizer.
@@ -125,7 +126,6 @@
    @classmethod
    def build_options(cls, args: argparse.Namespace) -> TrainerOptions:
        """Build options consumed by train(), eval()"""
        assert check_argument_types()
        return build_dataclass(TrainerOptions, args)

    @classmethod
@@ -142,11 +142,23 @@
        schedulers: Sequence[Optional[AbsScheduler]],
        scaler: Optional[GradScaler],
        ngpu: int = 0,
        oss_bucket=None,
    ):
        states = torch.load(
            checkpoint,
            map_location=f"cuda:{torch.cuda.current_device()}" if ngpu > 0 else "cpu",
        )
        if oss_bucket is None:
            if os.path.exists(checkpoint):
                states = torch.load(
                    checkpoint,
                    map_location=f"cuda:{torch.cuda.current_device()}" if ngpu > 0 else "cpu",
                )
            
            else:
                return 0
        else:
            if oss_bucket.object_exists(checkpoint):
                buffer = BytesIO(oss_bucket.get_object(checkpoint).read())
                states = torch.load(buffer, map_location=f"cuda:{torch.cuda.current_device()}" if ngpu > 0 else "cpu",)
            else:
                return 0
        model.load_state_dict(states["model"])
        reporter.load_state_dict(states["reporter"])
        for optimizer, state in zip(optimizers, states["optimizers"]):
@@ -165,7 +177,7 @@
    @classmethod
    def run(
        cls,
        model: AbsESPnetModel,
        model: FunASRModel,
        optimizers: Sequence[torch.optim.Optimizer],
        schedulers: Sequence[Optional[AbsScheduler]],
        train_iter_factory: AbsIterFactory,
@@ -174,7 +186,6 @@
        distributed_option: DistributedOption,
    ) -> None:
        """Perform training. This method performs the main process of training."""
        assert check_argument_types()
        # NOTE(kamo): Don't check the type more strictly as far trainer_options
        assert is_dataclass(trainer_options), type(trainer_options)
        assert len(optimizers) == len(schedulers), (len(optimizers), len(schedulers))
@@ -186,9 +197,6 @@
                logging.warning("No keep_nbest_models is given. Change to [1]")
                trainer_options.keep_nbest_models = [1]
            keep_nbest_models = trainer_options.keep_nbest_models
     
        #assert batch_interval is set and >0
        assert trainer_options.batch_interval > 0
 
        output_dir = Path(trainer_options.output_dir)
        reporter = Reporter()
@@ -208,15 +216,16 @@
        else:
            scaler = None

        if trainer_options.resume and (output_dir / "checkpoint.pb").exists():
        if trainer_options.resume:
            cls.resume(
                checkpoint=output_dir / "checkpoint.pb",
                checkpoint=os.path.join(trainer_options.output_dir, "checkpoint.pb") if trainer_options.use_pai else output_dir / "checkpoint.pb",
                model=model,
                optimizers=optimizers,
                schedulers=schedulers,
                reporter=reporter,
                scaler=scaler,
                ngpu=trainer_options.ngpu,
                oss_bucket=trainer_options.oss_bucket if trainer_options.use_pai else None,
            )

        start_epoch = reporter.get_epoch() + 1
@@ -269,14 +278,11 @@
        for iepoch in range(start_epoch, trainer_options.max_epoch + 1):
            if iepoch != start_epoch:
                logging.info(
                    "{}/{}epoch started. Estimated time to finish: {}".format(
                    "{}/{}epoch started. Estimated time to finish: {} hours".format(
                        iepoch,
                        trainer_options.max_epoch,
                        humanfriendly.format_timespan(
                            (time.perf_counter() - start_time)
                            / (iepoch - start_epoch)
                            * (trainer_options.max_epoch - iepoch + 1)
                        ),
                        (time.perf_counter() - start_time) / 3600.0 / (iepoch - start_epoch) * (
                                trainer_options.max_epoch - iepoch + 1),
                    )
                )
            else:
@@ -360,7 +366,7 @@
                            ],
                            "scaler": scaler.state_dict() if scaler is not None else None,
                            "ema_model": model.encoder.ema.model.state_dict()
                            if hasattr(model.encoder, "ema") and model.encoder.ema is not None else None,
                            if hasattr(model, "encoder") and hasattr(model.encoder, "ema") and model.encoder.ema is not None else None,
                        },
                        buffer,
                    )
@@ -539,7 +545,6 @@
        options: TrainerOptions,
        distributed_option: DistributedOption,
    ) -> Tuple[bool, bool]:
        assert check_argument_types()

        grad_noise = options.grad_noise
        accum_grad = options.accum_grad
@@ -549,8 +554,11 @@
        no_forward_run = options.no_forward_run
        ngpu = options.ngpu
        use_wandb = options.use_wandb
        bias_grad_times = options.bias_grad_times
        distributed = distributed_option.distributed

        if bias_grad_times != 1.0:
            logging.warning("Using bias_grad_times: {} for gradient scaling".format(bias_grad_times))
        if log_interval is None:
            try:
                log_interval = max(len(iterator) // 20, 10)
@@ -571,8 +579,7 @@
        #ouput dir
        output_dir = Path(options.output_dir)
        #batch interval
        batch_interval = options.batch_interval       
        assert batch_interval > 0
        batch_interval = options.batch_interval
 
        start_time = time.perf_counter()
        for iiter, (_, batch) in enumerate(
@@ -580,14 +587,23 @@
        ):
            assert isinstance(batch, dict), type(batch)

            if rank == 0:
            if batch_interval > 0 and (not distributed_option.distributed or rank == 0):
                if hasattr(model, "num_updates") or (hasattr(model, "module") and hasattr(model.module, "num_updates")):
                    num_batch_updates = model.get_num_updates() if hasattr(model,"num_updates") else model.module.get_num_updates()
                if (num_batch_updates%batch_interval == 0) and (options.oss_bucket is not None) and options.use_pai:
                    buffer = BytesIO()
                    torch.save(model.state_dict(), buffer)
                    options.oss_bucket.put_object(os.path.join(output_dir, f"{num_batch_updates}batch.pth"), buffer.getvalue())
 
                if num_batch_updates % batch_interval == 0:
                    if options.use_pai and options.oss_bucket is not None:
                        buffer = BytesIO()
                        if hasattr(model, "module"):
                            torch.save(model.module.state_dict(), buffer)
                        else:
                            torch.save(model.state_dict(), buffer)
                        options.oss_bucket.put_object(os.path.join(output_dir, f"{num_batch_updates}step.pb"), buffer.getvalue())
                    else:
                        if hasattr(model, "module"):
                            torch.save(model.module.state_dict(), os.path.join(output_dir, f"{num_batch_updates}step.pb"))
                        else:
                            torch.save(model.state_dict(), os.path.join(output_dir, f"{num_batch_updates}step.pb"))

            if distributed:
                torch.distributed.all_reduce(iterator_stop, ReduceOp.SUM)
                if iterator_stop > 0:
@@ -597,6 +613,24 @@
            if no_forward_run:
                all_steps_are_invalid = False
                continue

            if iiter == 1 and summary_writer is not None:
                try:
                    args = kwargs2args(model.forward, batch)
                except (ValueError, TypeError):
                    logging.warning(
                        "inpect.signature() is failed for the model. "
                        "The graph can't be added for tensorboard."
                    )
                else:
                    try:
                        summary_writer.add_graph(model, args, use_strict_trace=False)
                    except Exception:
                        logging.warning(
                            "summary_writer.add_graph() is failed for the model. "
                            "The graph can't be added for tensorboard."
                        )
                    del args

            with autocast(scaler is not None):
                with reporter.measure_time("forward_time"):
@@ -684,6 +718,16 @@
                        eta=1.0,
                        scale_factor=0.55,
                    )

                # for contextual training
                if bias_grad_times != 1.0:
                    # contextual related parameter names
                    cr_pnames = ["bias_encoder", "bias_embed", "decoder.bias_decoder", "decoder.bias_output"]
                    for name, param in model.named_parameters():
                        for cr_pname in cr_pnames:
                            if cr_pname in name:
                                param.grad *= bias_grad_times
                                continue

                # compute the gradient norm to check if it is normal or not
                grad_norm = torch.nn.utils.clip_grad_norm_(
@@ -794,7 +838,6 @@
        options: TrainerOptions,
        distributed_option: DistributedOption,
    ) -> None:
        assert check_argument_types()
        ngpu = options.ngpu
        no_forward_run = options.no_forward_run
        distributed = distributed_option.distributed